Method and system for intelligently recommending control schemes optimizing peak energy consumption of built environment

ABSTRACT

The present disclosure provides a computer-implemented method for recommending one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes analysis of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes recommendation of one or more control schemes to a plurality of energy consuming devices and a plurality of energy storage and supply means.

CROSS-REFERENCES TO RELATED APPLICATION

The present application claims the benefit under 35 U.S.C. § 119(e) ofthe filing date of U.S. Provisional Patent Application Ser. No.62/334,367 for PHILLIP KOPP, filed May 10, 2016, which is herebyincorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

The present disclosure relates to a field of energy management system.More specifically, the present disclosure relates to a method and systemfor recommending one or more control schemes to control peak loadingconditions and abrupt changes in energy pricing.

BACKGROUND

Over the last few decades, increasing population and energy requirementsto power modern transportation and electronic technologies result in arapid development in energy generation and distribution technology. Inorder to meet the energy generation and distribution requirements,energy utilities depend mostly on the non-renewable energy sources likefossil fuels which produce a high amount of carbon emissions. Refinementprocesses of fossil fuels and/or its by-products and their combustion todrive electric generators have contributed as one of the major cause ofexcessive carbon emissions. The release of carbon and other chemicalby-products into the atmosphere has impacted temperatures and climatepatterns on a global scale. The increased awareness of the impacts ofcarbon emissions from the use of fossil fueled electric generation alongwith the increased cost of producing high power during peak loadconditions has increased the need for alternative solutions. Thesealternative solutions are referred to as renewable energy sources, whichmay be used to generate electricity. These renewable energy sources maybe applied to electric drive trains, electric automation andtransportation, without the need to extract, transport, refine, combust,and release carbon-based fossil fuels. Renewable energy comes in manyforms, but significantly is generated by capturing energy from natural,non-carbon intensive sources such as wind, sunlight, water movement,geothermal and other new sources as they are discovered and improved.Unlike fossil fuels and/or its by-products, these renewable energysources are complex in nature as they are intermittent and cannot becontrolled actively by humans. This enhances the probability ofoccurrence of certain time periods where power production far exceedsdemands or certain time periods where power production falls short ofdemands. This creates major challenges for energy utilities to makeinvestments, generate power for sale and profit. Also, this createsmajor challenges for the markets in establishing a price of energy forconsumers. Nowadays, energy storage means are deployed to store energywhen power production is excessive and release energy when demandsexceed power production output. These energy storage means include butmay not be limited to batteries and sophisticated power banks. However,there are many limitations to the effective installation of the energystorage means due to sizing requirements, specific load profiles andother attributes which must be matched very carefully in order toprovide feasible economic returns.

SUMMARY

In a first example, a computer-implemented method is provided. Thecomputer-implemented method intelligently recommends one or more controlschemes for controlling peak loading conditions and abrupt changes inenergy pricing of one or more built environments associated withrenewable energy sources. The computer-implemented method may include afirst step of collection of a first set of statistical data associatedwith a plurality of energy consuming devices present in the one or morebuilt environments. In addition, the computer-implemented method mayinclude a second step of fetching a second set of statistical dataassociated with an occupancy behavior of a plurality of users presentinside each of the one or more built environments. Moreover, thecomputer-implemented method may include a third step of accumulating athird set of statistical data associated with each of a plurality ofenergy storage and supply means. Further, the computer-implementedmethod may include a fourth step of receiving a fourth set ofstatistical data associated with each of a plurality of environmentalsensors. Furthermore, the computer-implemented method may include afifth step of gathering a fifth set of statistical data associated witheach of a plurality of energy pricing models. Also, thecomputer-implemented method may include a sixth step of analyzing thefirst set of statistical data, the second set of statistical data, thethird set of statistical data, the fourth set of statistical data andthe fifth set of statistical data. In addition, the computer-implementedmethod may include a seventh step of recommending one or more controlschemes to the plurality of energy consuming devices and the pluralityof energy storage and supply means. The first set of statistical datamay include a current operational state data associated with theplurality of energy consuming devices and a past operational state dataassociated with the plurality of energy consuming devices. In addition,the first set of statistical data may be collected based on a firstplurality of parameters and the first set of statistical data iscollected in real time. The second set of statistical data may includean energy consumption behavior of each of the plurality of users presentinside the one or more built environments and an occupancy pattern ofeach of the plurality of users present inside the one or more builtenvironments. The third set of statistical data may include current andhistorical energy storage and supply capacity data associated with theplurality of energy storage and supply means. The third set ofstatistical data may be accumulated based on a second plurality ofparameters. The plurality of energy storage and supply means may includeat least one of batteries, high speed flywheels, pumped hydro energystorage means, thermal energy storage means and built environments. Thethird set of statistical data may be accumulated in the real time. Thefourth set of statistical data may include a current and historicalenvironmental condition data of at least one of inside and outside ofthe one or more built environments. The fourth set of statistical datamay be received based on a third plurality of parameters. The fourth setof statistical data may be received in the real time. The fifth set ofstatistical data may include current and historical recordings of energypricing state affecting the one or more built environments. The fifthset of statistical data may be gathered based on a fourth plurality ofparameters. The fourth set of statistical data may be gathered in thereal time. The analyzing may be done by performing one or morestatistical functions to generate a plurality of statistical results.The analyzing may be done in the real time. The one or more controlschemes may be recommended based on the plurality of statisticalresults. The one or more control schemes may include potentialoperational and non-operational instructions for optimizing theoperating state of the plurality of energy consuming devices andimproving the energy storage capacity of the plurality of energy storageand supply means.

In a second example, a computer system is provided. The computer systemmay include one or more processors and a memory coupled to the one ormore processors. The memory may store instructions which, when executedby the one or more processors, may cause the one or more processors toperform a method. The method intelligently recommends one or morecontrol schemes for controlling peak loading conditions and abruptchanges in energy pricing of one or more built environments associatedwith renewable energy sources. The method may include a first step ofcollection of a first set of statistical data associated with aplurality of energy consuming devices present in the one or more builtenvironments. In addition, the method may include a second step offetching a second set of statistical data associated with an occupancybehavior of a plurality of users present inside each of the one or morebuilt environments. Moreover, the method may include a third step ofaccumulating a third set of statistical data associated with each of aplurality of energy storage and supply means. Further, the method mayinclude a fourth step of receiving a fourth set of statistical dataassociated with each of a plurality of environmental sensors.Furthermore, the method may include a fifth step of gathering a fifthset of statistical data associated with each of a plurality of energypricing models. Also, the method may include a sixth step of analyzingthe first set of statistical data, the second set of statistical data,the third set of statistical data, the fourth set of statistical dataand the fifth set of statistical data. In addition, the method mayinclude a seventh step of recommending one or more control schemes tothe plurality of energy consuming devices and the plurality of energystorage and supply means. The first set of statistical data may includea current operational state data associated with the plurality of energyconsuming devices and a past operational state data associated with theplurality of energy consuming devices. In addition, the first set ofstatistical data may be collected based on a first plurality ofparameters and the first set of statistical data is collected in realtime. The second set of statistical data may include an energyconsumption behavior of each of the plurality of users present insidethe one or more built environments and an occupancy pattern of each ofthe plurality of users present inside the one or more builtenvironments. The third set of statistical data may include current andhistorical energy storage and supply capacity data associated with theplurality of energy storage and supply means. The third set ofstatistical data may be accumulated based on a second plurality ofparameters. The plurality of energy storage and supply means may includeat least one of batteries, high speed flywheels, pumped hydro energystorage means, thermal energy storage means and built environments. Thethird set of statistical data may be accumulated in the real time. Thefourth set of statistical data may include a current and historicalenvironmental condition data of at least one of inside and outside ofthe one or more built environments. The fourth set of statistical datamay be received based on a third plurality of parameters. The fourth setof statistical data may be received in the real time. The fifth set ofstatistical data may include current and historical recordings of energypricing state affecting the one or more built environments. The fifthset of statistical data may be gathered based on a fourth plurality ofparameters. The fourth set of statistical data may be gathered in thereal time. The analyzing may be done by performing one or morestatistical functions to generate a plurality of statistical results.The analyzing may be done in the real time. The one or more controlschemes may be recommended based on the plurality of statisticalresults. The one or more control schemes may include potentialoperational and non-operational instructions for optimizing theoperating state of the plurality of energy consuming devices andimproving the energy storage capacity of the plurality of energy storageand supply means.

In a third example, a computer-readable storage medium is provided. Thecomputer-readable storage medium encodes computer executableinstructions that, when executed by at least one processor, performs amethod. The method intelligently recommends one or more control schemesfor controlling peak loading conditions and abrupt changes in energypricing of one or more built environments associated with renewableenergy sources. The method may include a first step of collection of afirst set of statistical data associated with a plurality of energyconsuming devices present in the one or more built environments. Inaddition, the method may include a second step of fetching a second setof statistical data associated with an occupancy behavior of a pluralityof users present inside each of the one or more built environments.Moreover, the method may include a third step of accumulating a thirdset of statistical data associated with each of a plurality of energystorage and supply means. Further, the method may include a fourth stepof receiving a fourth set of statistical data associated with each of aplurality of environmental sensors. Furthermore, the method may includea fifth step of gathering a fifth set of statistical data associatedwith each of a plurality of energy pricing models. Also, the method mayinclude a sixth step of analyzing the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data. Inaddition, the method may include a seventh step of recommending one ormore control schemes to the plurality of energy consuming devices andthe plurality of energy storage and supply means. The first set ofstatistical data may include a current operational state data associatedwith the plurality of energy consuming devices and a past operationalstate data associated with the plurality of energy consuming devices. Inaddition, the first set of statistical data may be collected based on afirst plurality of parameters and the first set of statistical data iscollected in real time. The second set of statistical data may includean energy consumption behavior of each of the plurality of users presentinside the one or more built environments and an occupancy pattern ofeach of the plurality of users present inside the one or more builtenvironments. The third set of statistical data may include current andhistorical energy storage and supply capacity data associated with theplurality of energy storage and supply means. The third set ofstatistical data may be accumulated based on a second plurality ofparameters. The plurality of energy storage and supply means may includeat least one of batteries, high speed flywheels, pumped hydro energystorage means, thermal energy storage means and built environments. Thethird set of statistical data may be accumulated in the real time. Thefourth set of statistical data may include a current and historicalenvironmental condition data of at least one of inside and outside ofthe one or more built environments. The fourth set of statistical datamay be received based on a third plurality of parameters. The fourth setof statistical data may be received in the real time. The fifth set ofstatistical data may include current and historical recordings of energypricing state affecting the one or more built environments. The fifthset of statistical data may be gathered based on a fourth plurality ofparameters. The fourth set of statistical data may be gathered in thereal time. The analyzing may be done by performing one or morestatistical functions to generate a plurality of statistical results.The analyzing may be done in the real time. The one or more controlschemes may be recommended based on the plurality of statisticalresults. The one or more control schemes may include potentialoperational and non-operational instructions for optimizing theoperating state of the plurality of energy consuming devices andimproving the energy storage capacity of the plurality of energy storageand supply means.

BRIEF DESCRIPTION OF THE FIGURES

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an interactive environment for intelligentlyrecommending one or more control schemes associated with energyconsumption in one or more built environments, in accordance withvarious embodiments of the present disclosure;

FIG. 2 illustrates a block diagram for intelligently recommending theone or more control schemes associated with energy consumption in theone or more built environments, in accordance with various embodimentsof the present disclosure;

FIG. 3 illustrates a block diagram of a recommendation system, inaccordance with various embodiments of the present disclosure;

FIG. 4A and FIG. 4B illustrate a flow chart for intelligentlyrecommending one or more control schemes, in accordance with variousembodiments of the present disclosure; and

FIG. 5 illustrates a block diagram of a communication device, inaccordance with various embodiments of the present disclosure.

It should be noted that the accompanying figures are intended to presentillustrations of exemplary embodiments of the present disclosure. Thesefigures are not intended to limit the scope of the present disclosure.It should also be noted that accompanying figures are not necessarilydrawn to scale.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present technology. It will be apparent, however,to one skilled in the art that the present technology can be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form only in order to avoid obscuringthe present technology.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present technology. The appearance of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by some embodiments andnot by others. Similarly, various requirements are described which maybe requirements for some embodiments but not other embodiments.

Moreover, although the following description contains many specifics forthe purposes of illustration, anyone skilled in the art will appreciatethat many variations and/or alterations to said details are within thescope of the present technology. Similarly, although many of thefeatures of the present technology are described in terms of each other,or in conjunction with each other, one skilled in the art willappreciate that many of these features can be provided independently ofother features. Accordingly, this description of the present technologyis set forth without any loss of generality to, and without imposinglimitations upon, the present technology.

FIG. 1 illustrates an interactive environment for intelligentlyrecommending one or more control schemes associated with energyconsumption in one or more built environments, in accordance withvarious embodiment of the present disclosure. The interactiveenvironment facilitates assimilation and analysis of energy conditionsassociated with the one or more built environments. The energyconditions include but may not be limited to energy demand, energyconsumption, energy expenses and energy use intensity. The energyconditions are utilized for identification and recommendation of the oneor more control schemes for controlling peak loading conditions andabrupt changes in energy pricing associated with the one or more builtenvironments.

The interactive environment is characterized by the interaction of abuilt environment 102, a plurality of energy consuming devices 104, aplurality of energy storage and supply means 106, a plurality of sensors108 and one or more data collecting devices 110. In addition, theinteractive environment is characterized by the interaction of acommercial power supply grid node 112, one or more renewable energysupply sources 114 and a communication network 116. Furthermore, theinteractive environment is characterized by the interaction of aplurality of environmental sensors 118 and a plurality of energy pricingmodels 120. Moreover, the interactive environment is characterized bythe interaction of a recommendation system 122, a plurality of externalapplication program interfaces 124 (hereafter “APIs”) and one or morestatistical monitoring devices 126.

In general, the built environment 102 is a closed or semi-closedstructure with one or more number of floors utilized for specificpurposes. Each built environment are utilized to perform a pre-definedoperations and maintenance based on types of services provided by thebuilt environment 102. The types of services include hospitality,travel, work, entertainment, manufacturing and the like. In addition,each type of service provided decides a scale of the operations andmaintenance of the built environment 102. The type of services and themaintenance pertains to the energy consumption associated with each ofthe plurality of energy consuming devices 104. Examples of the builtenvironment 102 include but may not be limited to an office, a mall, anairport, a stadium, a hotel and a manufacturing plant.

The built environment 102 utilizes energy for operations and maintenanceof the built environment 102. The built environment 102 obtains theenergy from a plurality of energy generation and supply sources. Theplurality of energy generation and supply sources include but may not belimited to the commercial power supply grid node 112 and the one or morerenewable energy supply sources 114. The commercial power supply gridnode 112 corresponds to a network of power lines, a plurality oftransformers and one or more equipment employed for the transmission anddistribution of the alternate current power to the built environment102. Further, the one or more renewable energy supply sources 114include but may not be limited to one or more windmills and a pluralitysolar photovoltaic panels.

In an embodiment of the present disclosure, the one or more renewableenergy supply sources 114 are deployed at the built environment 102. Inan example, the plurality of solar photovoltaic panels are installed atthe residential or commercial rooftops. In another embodiment of thepresent disclosure, the one or more renewable energy supply sources 114are deployed at a remote location from the built environment 102. In anexample, the one or more windmills are deployed at countryside farmland.In an embodiment of the present disclosure, the one or more renewableenergy supply sources 114 is directly connected to the plurality ofenergy storage and supply means 106 associated with the builtenvironment 102. The one or more renewable energy supply sources 114directly provides DC energy to the plurality of energy storage andsupply means 106 without going through any voltage or current conversionprocess. In another embodiment of the present disclosure, the one ormore renewable energy supply sources 114 is connected with the builtenvironment 102 to supply available energy through the use of a directcurrent to alternating current inverter.

The plurality of energy storage and supply means 106 is configured tostore the energy and supply to fulfill energy demand associated with thebuilt environment 102. In an embodiment of the present disclosure, theplurality of energy storage and supply means 106 includes one or morebattery cells assembled to create one or more battery packs capable ofcharging and discharging electric energy. In another embodiment of thepresent disclosure, the plurality of energy storage and supply means 106is a high speed flywheel energy storage means. In yet another embodimentof the present disclosure, the plurality of energy storage and supplymeans 106 is pumped hydro energy storage means.

In yet another embodiment of the present disclosure, the plurality ofenergy storage and supply means 106 is non-electrical energy storingmediums. In an example, the energy storage and supply means may becomprised of thermal mass or momentum, such that the calculated amountof energy as converted to heat is stored within the energy storage andsupply means. In addition, the energy is stored and released at acertain rate using heat transfer or pumping as an energy transfermedium. In another example, a building environment, its construction,envelope and contents are utilized as a means to store, transfer andrelease energy passively or actively in a form of heat when combinedwith a means of artificial heating and cooling.

In an embodiment of the present disclosure, the plurality of energystorage and supply means 106 is located at a central location in thebuilt environment 102. The central location associated with the builtenvironment 102 includes an electrical room or closet, exterior in aspecialized storage cabinet or container and the like. In anotherembodiment of the present disclosure, the plurality of energy storageand supply means 106 is co-located with each of the plurality of energyconsuming devices 104. In yet another embodiment of the presentdisclosure, the plurality of energy storage and supply means 106 isdistributed throughout the built environment 102. In an example, theplurality of energy storage and supply means 106 is distributed instand-alone forms, plug-in forms and design oriented forms such asfurniture or permanent wall hanging forms or picture frames.

In yet another embodiment of the present disclosure, the plurality ofenergy storage and supply means 106 is built into the building structureor building electrical distribution itself. In yet another embodiment ofthe present disclosure, the plurality of energy storage and supply means106 is in a form of thermal heat mass capture and release usingcalculated capacities of building materials. In yet another embodimentof the present disclosure, the plurality of energy storage and supplymeans 106 is located outside of the built environment 102 in amicro-grid or fractal grid application.

Going further, the built environment 102 is associated with a pluralityof users 128 present inside the built environment 102. The plurality ofusers 128 may be any human operator, human worker, occupants, datamanager, visitors and the like. Each of the plurality of users 128 isassociated with a task. For example, the human operators perform thetask of monitoring and regulating machines. In another example, thehuman workers perform the task of cleaning, sweeping and repairing. Inyet another example, the occupants are the employees that includemanagers, attendants, assistants, clerk, security staff and the like. Inyet another example, the visitors are civilians present for a specificperiod of time.

Each of the plurality of users 128 utilizes a pre-defined amount of theenergy. The pre-defined amount of the energy pertains to a correspondingenergy consuming device of the plurality of energy consuming devices104. Moreover, each of the plurality of energy consuming devices 104performs an operation to meet requirements of the plurality ofoperations associated with the built environment 102. The plurality ofoperations is associated with the operation of each of the plurality ofenergy consuming devices 104 installed in the built environment 102. Theplurality of energy consuming devices 104 may be of any type and size.In addition, the plurality of energy consuming devices 104 includes aplurality of electrical devices and a plurality of portablecommunication devices.

In an embodiment of the present disclosure, the plurality of energyconsuming devices 104 may have any electrical and mechanicalapplications. Examples of the plurality of energy consuming devices 104include but may not be limited to lighting circuits, refrigerationunits, air conditioning systems, information technology networks, gasboilers, hot water heater, escalators, and elevators. The plurality ofenergy consuming devices 104 consumes a pre-defined amount of the energybased on a power rating, duration of energy usage and the plurality ofoperations performed. The pre-defined amount of the energy consumed bythe plurality of energy consuming devices 104 is based on one or moreenergy physical variables. The one or more energy physical variablesinclude but may not be limited to a power factor, a phase angle, a powerfrequency, a voltage, a current load and a power demand.

The one or more energy physical variables of each of the plurality ofenergy consuming devices 104 is monitored and measured by a plurality ofenergy metering devices. Each of the plurality of energy consumingdevices 104 is combined with the plurality of energy metering devices.In an embodiment of the present disclosure, the plurality of energymetering devices is installed inside each of the plurality of energyconsuming devices 104. The plurality of energy metering devices measureseach of the one or more energy physical variables in real time. Theplurality of energy metering devices include but may not be limited todigital multi-meters, current sensors and wattage meters. In addition,the plurality of energy metering devices facilitates collection of afirst set of statistical data associated with the plurality of energyconsuming devices 104.

The collection of the first set of statistical data uses a method. In anembodiment of the present disclosure, the method involves digitalcollection of the first set of statistical data for each of theplurality of energy consuming devices 104. In another embodiment of thepresent disclosure, the method involves physical collection of the firstset of statistical data for each of the plurality of energy consumingdevices 104. The plurality of energy metering devices monitors a firstplurality of parameters. The first plurality of parameters is associatedwith the plurality of energy consuming devices 104. The first pluralityof parameters includes a set of operational characteristics and a set ofphysical characteristics. The set of operational characteristics includea current rating, a voltage rating, a power rating, a frequency ofoperation, an operating temperature and a device temperature. Inaddition, the set of operational characteristics include a duration ofthe energy usage by each of the plurality of energy consuming devices104 in the built environment 102. Moreover, the set of operationalcharacteristics include a seasonal variation in operation and anoff-seasonal variation in operation. Further, the set of physicalcharacteristics include a device size, a device area, a device physicallocation and a portability of device. In an embodiment of the presentdisclosure, the one or more energy metering devices collects the firstset of statistical data. In addition, the first set of statistical dataincludes a current operational state data and a past operational statedata. The current operational state data and the past operational statedata corresponds to current energy consumption data and the historicalenergy consumption data associated with the plurality of energyconsuming devices 104 of the built environment 102.

Going further, the plurality of energy consuming devices 104 isassociated with the plurality of users 128. The plurality of users 128interacts with the plurality of energy consuming devices 104 installedin the built environment 102 to perform specific operations. The dailyusage and the operating characteristics of the plurality of energyconsuming devices 104 are derived from an interface associated with eachuser of the plurality of users 128. Each of the plurality of energyconsuming devices 104 consumes a pre-defined amount of energy during theinterface. The pre-defined amount of energy is derived based on anenergy consumption behavior and an occupancy pattern of each of theplurality of users 128. In an example, each user of the plurality ofusers 128 in the built environment 102 may arrive and leave the builtenvironment 102 during certain hours each day. Each user carries one ormore portable communication devices both in and out of the builtenvironment 102.

Further, the energy consumption behavior and the occupancy pattern isrecorded for each of the plurality of users 128 to obtain a second setof statistical data. The energy consumption behavior and occupancypattern is collected and recorded by a plurality of occupancy detectionmeans. The plurality of occupancy detection means collect the energyconsumption behavior and occupancy pattern associated with the pluralityof users 128 in real time. The plurality of occupancy detection meansare installed inside and outside of the built environment 102. Theplurality of occupancy detection means include a plurality of occupancysensing devices. The plurality of occupancy sensing devices includeoccupancy sensors, door state sensors, motion detectors, microphones,radio frequency identification (hereinafter as “RFID”), radio receivedsignal strength indicators (hereinafter as “RSSI”) and digital or radiofrequency signal processors. Furthermore, the plurality of occupancydetection means include the plurality of sensors 108. The plurality ofsensors 108 include carbon-monoxide sensors, carbon-dioxide sensors,heat sensors, pressure sensors, atmospheric pressure sensors,temperature sensors, energy flow sensors, energy fingerprint sensors onmonitored loads physical touch point sensors and the like.

The first set of statistical data and the second set of statistical datais transferred to the one or more data collecting devices 110 associatedwith the built environment 102. The one or more data collecting devices110 collects the first set of statistical data and the second set ofstatistical data. The one or more data collecting devices 110 performdigital collection and manual collection. In an embodiment of thepresent disclosure, each of the one or more data collecting devices 110is a portable device with an inbuilt API. The inbuilt API of each of theone or more data collection devices 110 is associated with a GlobalPositioning system (hereafter “GPS”). Further, the inbuilt API of eachof the one or more data collection devices 110 is associated with acamera and keypad designed for manual data input from the plurality ofusers 128. In another embodiment of the present disclosure, each of theone or more data collecting devices 110 is a cellular modem. In yetanother embodiment of the present disclosure, each of the one or moredata collecting devices 110 is any suitable data gateway device.

The one or more data collecting devices 110 collects a third set ofstatistical data associated with each of the plurality of energy storageand supply means 106. In an embodiment of the present disclosure, theone or more data collecting devices 110 receives the third set ofstatistical data from the plurality of energy monitoring devicesassociated with each of the plurality of energy storage and supply means106. The plurality of energy monitoring devices monitor a secondplurality of parameters associated with the plurality of energy storageand supply means 106. In addition, the plurality of energy monitoringdevices collect and transfer the second plurality of parametersassociated with the plurality of energy storage and supply means 106 tothe one or more data collecting devices 110 in real time. The secondplurality of parameters include but may not be limited to charging anddischarging rates, temperature characteristics, an energy storage andrelease capacity associated with the plurality of energy storage.

The one or more data collecting devices 110 is associated with thecommunication network 116 through an internet connection. The internetconnection is established based on a type of network. In an embodimentof the present disclosure, the type of network is a wireless mobilenetwork. In another embodiment of the present disclosure, the type ofnetwork is a wired network with a finite bandwidth. In yet anotherembodiment of the present disclosure, the type of network is acombination of the wireless and the wired network for the optimumthroughput of data transmission. The communication network 116 includesa set of channels with each channel of the set of channels supporting afinite bandwidth. The finite bandwidth of each channel of the set ofchannels is based on a capacity of the communication network 116. Thecommunication network 116 transmits a pre-defined size of the first setof statistical data, the second set of statistical data and the thirdset of statistical data to the recommendation system 122. Thepre-defined size corresponding to the first set of statistical data, thesecond set of statistical data and the third set of statistical data ismeasured in terms of at least one of bits, bytes, kilobytes, megabytes,gigabytes, terabytes and petabytes. Accordingly, the recommendationsystem 122 receives the pre-defined size of the first set of statisticaldata, the second set of statistical data and the third set ofstatistical data. In addition, the recommendation system 122 receivesanother part of the first set of statistical data, the second set ofstatistical data and the third set of statistical data from theplurality of external APIs 124 and third party databases.

Continuing with FIG. 1, the recommendation system 122 receives a fourthset of statistical data and a fifth set of statistical data. Therecommendation system 122 receives the fourth set of statistical datafrom the plurality of environmental sensors 118 through thecommunication network 116. The plurality of environmental sensors 118detect and collect environmental and weather conditions associated withthe built environment 102 in real time. In addition, the plurality ofenvironmental sensors 118 transfers the environmental and weatherconditions to the recommendation system 122 in real time. In anembodiment of the present disclosure, the plurality of environmentalsensors 118 is present inside the built environment 102. In anotherembodiment of the present disclosure, the plurality of environmentalsensors 118 is present outside the built environment 102. Further, therecommendation system 122 receives the fifth set of statistical datafrom the plurality of energy pricing models 120. The plurality of energypricing models 120 is configured to record energy prices associated withthe built environment 102.

The recommendation system 122 receives another part of the fourth set ofstatistical data and the fifth set of statistical data from theplurality of external APIs 124 and third party databases. The pluralityof external APIs 124 and the third party databases are configured tocollect, store and transmit weather history and weather forecasts. Inaddition, the plurality of external APIs 124 and the third partydatabases are configured to collect, store and transmit billing data, apast energy consumption data and metered energy data. Furthermore, theplurality of external APIs 124 and the third party databases areconfigured to collect, store and transmit financial or non-financialbusiness data. The financial or non-financial business data comes frombusiness management software. Example of the business managementsoftware includes Enterprise Resources Planning (ERP) software.

The recommendation system 122 analyzes the first set of statisticaldata, the second set of statistical data, the third set of statisticaldata, the fourth set of statistical data and the fifth set ofstatistical data. The analysis is done by performing one or morestatistical functions (discussed below in detailed description of FIG.2). The recommendation system 122 performs the one or more statisticalfunctions to generate a plurality of statistical results. The pluralityof statistical results pertains to the energy consumption (discussedbelow in detailed description of FIG. 2). The plurality of statisticalresults obtained from the analysis is used as a reference basis of theenergy consumption to recommend the one or more control schemes forcontrolling peak loading conditions and abrupt changes in energypricing.

Further, the recommendation system 122 displays the plurality ofstatistical results through an application installed in a mobile phone,tablet, smart watch and the like. In another embodiment of the presentdisclosure, the recommendation system 122 displays each of the pluralityof statistical results on a web page. In yet another embodiment of thepresent disclosure, the recommendation system 122 displays each of theplurality of statistical results on a plurality of monitors.Furthermore, the recommendation system 122 recommends the one or morecontrol schemes to the plurality of energy consuming devices and theplurality of energy controlling devices. The one or more control schemespertain to the energy consumption of each of the plurality of energyconsuming devices 104 present inside the built environment 102(explained below in the detailed description of the FIG. 2).

Further, the recommendation system 122 transfers the plurality ofstatistical results along with the one or more control schemes to theone or more statistical monitoring devices 126. The one or morestatistical monitoring devices 126 is configured to receive and displayat least one of the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data and the fifth set of statistical data. In addition, theone or more statistical monitoring devices 126 are configured to receiveand display at least one of the plurality of statistical results and theone or more control schemes for proper monitoring and regulation. Theone or more statistical monitoring devices 126 is a device capable ofreceiving the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data and the fifth set of statistical data from therecommendation system 122. Also, the one or more statistical monitoringdevices 126 is a device capable of receiving the plurality ofstatistical results and the one or more control schemes from therecommendation system 122.

It may be noted that in FIG. 1, the recommendation system 122 transfersthe first set of statistical data, the second set of statistical data,the third set of statistical data, the fourth set of statistical data,the fifth set of statistical data, the plurality of statistical resultsand the one or more control schemes to the one or more statisticalmonitoring devices 126; however, those skilled in the art wouldappreciate that the recommendation system 122 transfers the first set ofstatistical data, the second set of statistical data, the third set ofstatistical data, the fourth set of statistical data and the fifth setof statistical data, the plurality of statistical results and the one ormore control schemes to more number of statistical monitoring devices.Furthermore, it may be noted that in FIG. 1, the built environment 102is connected to the recommendation system 122 through the communicationnetwork 116; however, those skilled in the art would appreciate thatmore number of built environments are connected to the recommendationsystem 122 through the communication network 116.

FIG. 2 illustrates a block diagram 200 for intelligently recommendingone or more control schemes to control peak loading conditions andabrupt changes in energy pricing of the built environment 102, inaccordance with various embodiments of the present disclosure. It may benoted that to explain the system elements of FIG. 2, references will bemade to the system elements of the FIG. 1.

The block diagram 200 includes the built environment 102, commercialpower supply grid node 112, the one or more renewable energy supplysources 114, the recommendation system 122 and the plurality of externalAPIs 124 (as discussed above in detailed description of FIG. 1). Inaddition, the block diagram 200 includes the plurality of environmentalsensors 118 and the plurality of energy pricing models 120 and the oneor more statistical monitoring devices 126 (as discussed above indetailed description of FIG. 1). Moreover, the block diagram 200includes a network based automatic control system 202 and third partydatabases 206. Furthermore, the recommendation system 122 includes aserver 204. In addition, the server 204 includes a database 204 a and aprocessor 204 b.

Each of the plurality of energy consuming devices 104 is associated withone or more energy physical variables (as described above in detaileddescription of FIG. 1). The one or more energy physical variablesdefines the energy consumption in the real time based on the load. In anembodiment of the present disclosure, each of the plurality of energyconsuming devices 104 is associated with the plurality of energymetering devices. The plurality of energy metering devices digitallymeasures one or more energy physical variables in the real time toobtain the first set of statistical data (as discussed above in detaileddescription of FIG. 1). The plurality of energy metering devicesincludes one or more digital meters, one or more digital current andvoltage sensors, the multi-meters, watt-meters, supervisory control anddata acquisition (SCADA) and the like.

The recommendation system 122 collects the first set of statistical dataassociated with the plurality of energy consuming devices 104 from theplurality of energy metering devices. The first set of statistical dataincludes a current operational state data associated with the pluralityof energy consuming devices 104 and a past operational state dataassociated with the plurality of energy consuming devices 104. Theoperational state data is associated with the pre-defined amount ofenergy consume by each of the plurality of energy consuming devices 104in real time. The plurality of energy consuming devices 104 consume thepre-defined amount of energy to perform a specific operation (asdiscussed above in detailed description of FIG. 1).

Further, the recommendation system 122 fetches the second set ofstatistical data associated with an occupancy behavior of the pluralityof users 128 present inside each of the built environment 102. Therecommendation system 122 fetches the second set of statistical datafrom the plurality of occupancy detection means. The second set ofstatistical data includes the energy consumption behavior of each of theplurality of users 128 present inside the built environment 102. Inaddition, the second set of statistical data includes the occupancypattern of each of the plurality of users 128 present inside the builtenvironment 102. The energy consumption behavior and occupancy patternis recorded and counted by the plurality of occupancy detection means toobtain the second set of statistical data (as described above indetailed description of FIG. 1). In addition, the plurality of occupancydetection means record and count based on the one or morespecifications. The one or more specifications include heat signature,identification cards, Bluetooth and the like. In an example, the recordof first time visitors and frequent visitors is maintained for fastercollection of the second set of statistical data. Further, the energyusage pattern of each of the plurality of users 128 creates a unique andaggregated consumption of the energy. The unique and aggregatedconsumption of the energy is based on a variation in number of theplurality of users 128. The variation in the number of the plurality ofusers 128 is based on days, months, seasons, events and time of year. Inaddition, the variation in the number of the plurality of users 128 maybe based on architectural configurations of the built environment 102.In an example, the occupancy pattern of the plurality of users 128 inshopping malls increases during the festive seasons. In another example,the occupancy pattern at the soccer ground increases during the matchday.

Further, the recommendation system 122 accumulates the third set ofstatistical data associated with each of the plurality of energy storageand supply means 106 from the plurality of energy monitoring devices.The third set of statistical data includes a current and historicalenergy storage and supply capacity data associated with the plurality ofenergy storage and supply means 106. The plurality of energy monitoringdevices record and collect energy storage and supply capacity dataassociated with the plurality of energy storage and supply means 106 toobtain the third set of statistical data. The recommendation system 122accumulates the third set of statistical data based on the secondplurality of parameters (as mentioned above in detailed description ofFIG. 1).

The recommendation system 122 receives the fourth set of statisticaldata from the plurality of environmental sensors 118 associated with thebuilt environment 102 (discussed above in detailed description of FIG.1). In addition, the recommendation system 122 receives the fourth setof statistical data from the plurality of external APIs 124 and thethird party databases 206. The fourth set of statistical data includesthe current and historical environmental condition data of at least oneof inside and outside of the built environment 102. The fourth set ofstatistical data is received by the recommendation system 122 based on athird plurality of parameters. In an embodiment of the presentdisclosure, the third plurality of parameters include but may not belimited to a metric for recording environmental data having temperature,humidity and air pressure associated with each of the plurality ofenvironmental sensors.

Further, the recommendation system 122 gathers the fifth set ofstatistical data from the plurality of energy pricing models 120. Thefifth set of statistical data includes current and historical recordingsof energy pricing state affecting the built environment 102. Theplurality of energy pricing models 120 record and transfer the energypricing to the recommendation system 122 in real time through thecommunication network 116. In addition, the recommendation system 122gathers the fifth set of statistical data from the plurality of externalAPIs 124 and the third party databases 206 (as discussed above indetailed description of FIG. 1). The fifth set of statistical data isgathered based on a fourth plurality of parameters. In an embodiment ofthe present disclosure, the fourth plurality of parameters include ametric for recording energy pricing data having an energy pricing model,an energy price signal associated with the built environment 102.

Going further, the recommendation system 122 performs the analysis ofthe first set of statistical data, the second set of statistical data,the third set of statistical data, the fourth set of statistical dataand the fifth set of statistical data. The recommendation system 122performs the one or more statistical functions to generate the pluralityof statistical results. The one or more statistical functions includetranslating the current operational state data and the past operationalstate data associated with the plurality of energy consuming devices 104into energy demand values. In addition, the one or more statisticalfunctions include parsing the first set of statistical data, the secondset of statistical data and the third set of statistical data. Therecommendation system 122 develops an energy usage profile. Therecommendation system 122 develops the energy usage profile of each ofthe plurality of energy consuming devices 104, each of the plurality ofenergy storage and supply means 106 and each of the plurality of users128. In addition, the recommendation system 122 develops the energyusage profile associated with each zone of the floor, each group ofzones of floor, each floor of a building and each of the one or morebuilt environments.

Further, the one or more statistical functions include auto-fulfillingone or more data entries in the first set of statistical data, thesecond set of statistical data and the third set of statistical data.The auto-fulfilling of the one or more data entries is performed tominimize errors in deriving the energy consumption and demand associatedwith the built environment 102 for a given time interval. Moreover, therecommendation system 122 auto-fulfils the one or more data entries byusing an application of at least one of the statistical regression,interpolation and extrapolation.

The one or more statistical functions include comparing the currentoperational state data with the past operational state data. Therecommendation system 122 compares the current operational state datawith the past operational state data associated with the each of theplurality of energy consuming devices 104. The current operational statedata and the past operational state data are compared to determine thepotential for improvement in energy consumption of each of the pluralityof energy consuming devices 104. In addition, the recommendation system122 compares a current energy storage capacity and a past energy storagecapacity associated with each of the plurality of energy storage andsupply means 106. The current energy storage capacity and the pastenergy storage capacity are compared to determine the potential forimprovement in charge/discharge cycles and energy storage capacity ofeach of the plurality of energy storage means 106.

Accordingly, the analysis of the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical dataprovides the plurality of statistical results. The plurality ofstatistical results pertains to the energy consumption. In addition, theplurality of statistical results is based on a statistical data model.The statistical data model provides a complete insight into the energyconsumption trend. The plurality of statistical results includes one ormore graphs, one or more charts, one or more tables and one or morestatistical maps of energy consumption. The plurality of statisticalresults is obtained as a function of duration of the operations of theplurality of energy consuming devices and energy storage and supplycapacity of the plurality of energy storage and supply means 106. Inaddition, the plurality of statistical results is obtained as a functionof environmental conditions, and energy pricing affecting the builtenvironment 102.

In an example, the plurality of statistical results include a table andchart of monthly energy consumption of the built environment 102 and atable of a total monthly variable energy load. In another example, theplurality of statistical results includes a pie chart to show aseparation of the energy use in the built environment 102 and a table ofenergy consumption per month and air conditioner loads. In yet anotherexample, the plurality of statistical results includes a statisticalchart depicting a kWh consumption based on load type, a bar graph ofexpected air conditioner savings and service costs. In yet anotherexample, the plurality of statistical results include a bar chart ofgross rental, service and licensing costs of at least one of airconditioning units, air conditioning control means, statistical softwareand networks. In yet another example, the plurality of statisticalresults includes the statistical chart of total kWh consumed per room asa function of cold degree days.

Further, the recommendation system 122 recommends the one or morecontrol schemes to each of the plurality of energy consuming devices 104associated with the built environment 102. In addition, therecommendation system 122 recommends the one or more control schemes toeach of the plurality of energy storage and supply means 106 associatedwith the built environment 102. The recommendation system 122 recommendsthe one or more control schemes recommended based on the plurality ofstatistical results. The one or more control schemes are recommended tocontrol the peak loading conditions and abrupt changes in the energypricing associated with the built environment 102.

The one or more control schemes includes a potential operational andnon-operational instructions for optimizing the operating state of theplurality of energy consuming devices 104. In addition, the one or morecontrol schemes includes the potential operational and non-operationalinstructions for improving the energy storage capacity of the pluralityof energy storage and supply means 106. The potential operational andnon-operational instructions include regulating power supply of each ofthe plurality of energy consuming devices 104 based on an occupancypattern, energy demand and architectural design of the built environment102. In addition, the potential operational and non-operationalinstructions include regulating energy consumption duration of theplurality of energy consuming devices 104. Moreover, the potentialoperational and non-operational instructions include notifying a list ofmalfunctioning devices of the plurality of energy consuming devices 104.Furthermore, the potential operational and non-operational instructionsinclude performing an operation on the plurality of energy consumingdevices. The operation is selected from a group of operations consistingof upgrading, downgrading, replacing and repairing of the plurality ofenergy consuming devices.

Further, the operational and non-operational instructions includeprompting the plurality of energy storage and supply means 106 to startand stop charge cycles at specific time periods for reducing energyconsumption costs. In addition, the operational and non-operationalinstructions include prompting the plurality of energy storage andsupply means 106 to start and stop discharge cycles for controlling thepeak loading periods. Moreover, the operational and non-operationalinstructions include regulating the charging and dischargingcharacteristics of each of the plurality of energy storage and supplymeans 106.

The recommendation system 122 recommends the one or more control schemesthrough the communication network 116. In an embodiment, therecommendation system 122 recommends the one or more control schemesthrough the network based automatic control system 202. The networkbased automatic control system 202 is associated with the builtenvironment 102. In addition, the network based automatic control system202 is associated with a plurality of electrical control relays. Inaddition, the network based automatic control system 202 is associatedwith a microprocessor based switches. The network based automaticcontrol system 202 sends one or more control signals based on the one ormore control schemes. The network based automatic control system 202automatically applies the one or more control schemes to the builtenvironment 102. The network based automatic control system 202 controlsthe operation of each of the plurality of energy consuming devices 104.In addition, the network based automatic control system 202 controls theplurality of energy consuming devices 104 based on the occupancybehavior of the plurality of users 128 and energy storage capacity ofthe plurality of energy storage and supply means 106. Moreover, thenetwork based automatic control system 202 controls the plurality ofenergy consuming devices 104 based on weather conditions and real timeenergy pricing associated with the built environment 102. Furthermore,the network based automatic control system 202 controls the plurality ofenergy storage and supply means 106 based on the real time energydemand, weather conditions and forecasts, and real time energy pricingassociated with the built environment 102.

In an example of a hotel, which may be mostly unoccupied during thedaytime, the recommendation system 122 recommends an operationalinstruction to take substantial amount of loads out of service withoutaffecting comfort of the occupant. In addition, the recommendationsystem 122 recommends the operational instruction to energy storage andsupply means installed in the hotel to perform longer charge cycles. Therecommendation system 122 recommends turning off the maximum number ofair conditioning units possible in areas which were read to beunoccupied. However, noting, that as the temperature began to rise tounacceptable levels the recommendation system 122 would be able toautomatically adjust the maximum number of air conditioning units thatwere taken out of service at any particular interval. At that particulartime interval, the recommendation system 122 instruct the energy storageand supply means to begin its discharge cycle so that the airconditioning units could be re-started and put back into service untilthe hotel interior reached an acceptable level.

Further, the recommendation system 122 provides the improvement in therecommendations of the one or more control schemes. The improvement inthe recommendations is obtained from a learning algorithm. The learningalgorithm accelerates assessment and the analysis of one or more datapoints. The one or more data points associate with the energyconsumption of each of the plurality of energy consuming devices 104 andeach of the plurality of energy storage and supply means 106 associatedwith the built environment 102. The recommendation system 122 utilizesthe one or more data points to create a continuous closed control andfeedback loop for optimizing the operating state of the plurality ofenergy consuming devices 104. In addition, the recommendation system 122utilizes the one or more data points to create a continuous closedcontrol and feedback loop for improving the energy storage capacity ofthe plurality of energy storage and supply means 106.

The recommendation system 122 stores the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data and the fifth set of statistical datain the database 204 a. In addition, the recommendation system 122 storesthe plurality of statistical results and a log file having one or morecontrol schemes in the database 204 a. Moreover, the recommendationsystem 122 stores the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results and the log file having one or more control schemesin real time.

The recommendation system 122 updates the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data and the fifth set of statisticaldata. In addition, the recommendation system 122 updates the pluralityof statistical results and the log file having one or more controlschemes. Moreover, the recommendation system 122 updates the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data, the fifth setof statistical data, the plurality of statistical results and the logfile having one or more control schemes in real time.

The recommendation system 122 displays the first set of statisticaldata, the second set of statistical data, the third set of statisticaldata, the fourth set of statistical data and the fifth set ofstatistical data on the one or more statistical monitoring devices 126.In addition, the recommendation system 122 displays the plurality ofstatistical results and the log file having one or more control schemeson the one or more statistical monitoring devices 126. Moreover, therecommendation system 122 displays the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data, the fifth set of statistical data,the plurality of statistical results and the log file having one or morecontrol schemes in real time.

FIG. 3 illustrates a block diagram 300 of the recommendation system 122,in accordance with various embodiment of the present disclosure. It maybe noted that to explain the system elements of FIG. 3, references willbe made to the system elements of the FIG. 1 and the FIG. 2. Therecommendation system 122 includes a collection module 302, a fetchingmodule 304, an accumulation module 306, a reception module 308, agathering module 310 and an analyzing module 312. In addition, therecommendation system 122 includes a recommendation module 314, astorage module 316, an updating module 318 and a displaying module 320.The above mentioned modules are configured for intelligentlyrecommending the one or more control schemes for controlling the peakloading conditions and the abrupt changes in energy pricing of the builtenvironment 102.

The collection module 302 collects the first set of statistical dataassociated with each of the plurality of energy consuming devices 104installed in the built environment 102. The first set of statisticaldata includes the current operational state data and the pastoperational state data associated with the plurality of energy consumingdevices 104. The plurality of energy metering devices collects the firstset of statistical data. The plurality of energy metering devicestransfer the first set of statistical data to the one or more datacollecting devices 110. The one or more data collecting devices 110transfer the first set of statistical data to the recommendation system122 (as explained above in the detailed description of FIG. 1 and FIG.2).

The fetching module 304 fetches the second set of statistical dataassociated with the occupancy behavior of the plurality of users 128present inside the built environment 102. The second set of statisticaldata includes the energy consumption behavior of each of the pluralityof users 128 present inside the built environment 102. In addition, thesecond set of statistical data includes the occupancy pattern of each ofthe plurality of users 128 present inside the built environment 102. Theplurality of occupancy detection means and the plurality of sensors 108fetches the second set of statistical data in real time. In addition,the plurality of occupancy detection means and the plurality of sensors108 transfer the second set of statistical data to the recommendationsystem 122 (as discussed above in detailed description of FIG. 1 andFIG. 2).

The accumulation module 306 accumulates the third set of statisticaldata associated with each of the plurality of energy storage and supplymeans 106 associated with the built environment 102. The third set ofstatistical data includes the current and historical energy storage andsupply capacity data associated with the plurality of energy storage andsupply means 106. The plurality of energy monitoring devices accumulatesthe energy storage and supply capacity data associated with each of theplurality of energy storage and supply means 106 in real time to obtainthe third set of statistical data. In addition, the plurality of energymonitoring devices transfer the third set of statistical data to therecommendation system 122 (as explained above in detailed description ofFIG. 1 and FIG. 2).

The reception module 308 receives the fourth set of statistical dataassociated with each of the plurality of environmental sensors 118. Thefourth set of statistical data includes the current and historicalenvironmental condition data of at least one of inside and outside ofthe built environment 102. The plurality of environmental sensors 118records the environmental condition data in real time to obtain thefourth set of statistical data. In addition, the plurality ofenvironmental sensors 118 transfers the fourth set of statistical datato the recommendation system 122. Moreover, the reception module 308receives the fourth set of statistical data from the plurality ofexternal APIs 124 and the third party databases 206 (as discussed abovein detailed description of FIG. 1 and FIG. 2).

The gathering module 310 gathers the fifth set of statistical dataassociated with each of the plurality of energy pricing models 120. Thefifth set of statistical data includes the current and historicalrecordings of the energy pricing state affecting the built environment102. The plurality of energy pricing models 120 record the real timeenergy pricing state associated with the built environment 102 to obtainthe fifth set of statistical data. In addition, the plurality of energypricing models transfers the fifth set of statistical data to therecommendation system 122. Moreover, the gathering module 310 gathersthe fifth set of statistical data from the plurality of external APIs124 and the third party databases 206 (as explained above in detaileddescription of FIG. 1 and FIG. 2).

The analyzing module 312 analyzes the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data.The analyzing module 312 includes a translation module 312 a, a parsingmodule 312 b, an auto-fulfilling module 312 c and a comparison module312 d. The translation module 312 a translates the current operationalstate data and the past operational state data associated with theplurality of energy consuming devices 104 into the energy demand values.In addition, the translation module 312 a translates the currentoperational state data and the past operational state data into theenergy demand values for the pre-defined interval of time (as discussedin detailed description of FIG. 1 and FIG. 2).

Further, the parsing module 312 b parses the first set of statisticaldata, the second set of statistical data and the third set ofstatistical data. The parsing module 312 b parses the first set ofstatistical data, the second set of statistical data and the third setof statistical data based on the physical location of each of theplurality of energy consuming devices 104. In addition, the parsingmodule 312 b parses the first set of statistical data, the second set ofstatistical data and the third set of statistical data based on theoccupancy pattern of the plurality of users 128. Moreover, the parsingmodule 213 b parses the first set of statistical data, the second set ofstatistical data and the third set of statistical data based on theweather conditions and real time energy pricing state (as explainedabove in the detailed description of FIG. 1 and FIG. 2).

Further, the auto-fulfilling module 312 c auto-fulfills the one or moredata entries in the first set of statistical data, the second set ofstatistical data and the third set of statistical data (as discussedabove in detailed description of FIG. 1 and FIG. 2). Furthermore, thecomparison module 312 d compares the current operational state data withthe past operational state data associated with the each of theplurality of energy consuming devices 104. In addition, the comparisonmodule 312 d compares the current energy storage capacity and the pastenergy storage capacity associated with each of the plurality of energystorage and supply means 106 (as discussed above in detailed descriptionof FIG. 1 and FIG. 2).

The analysis is performed to generate the plurality of statisticalresults associated with the energy consumption of the built environment102 in real time. The plurality of statistical results includes one ormore graphs, one or more charts, one or more tables and one or morestatistical maps of the energy consumption as a function of duration ofthe operations. Further, the plurality of statistical results includesbase-load, variable load, the cost of the operations, energy efficiency,the temperature, humidity and daylight. Furthermore, the plurality ofstatistical results includes the real time occupancy of the plurality ofusers 128 inside the built environment 102 and physical parameters ofeach of the plurality of energy consuming devices.

The recommendation module 314 recommends the one or more control schemesto each of the plurality of energy consuming devices 104 and theplurality of energy storage and supply means 106. The recommendationmodule 314 recommends the one or more control schemes based on theplurality of statistical results. The one or more control schemesincludes the potential operational and non-operational instructions foroptimizing the operating state of the plurality of energy consumingdevices 104. In addition, the one or more control schemes includes thepotential operational and non-operational instructions for improving theenergy storage capacity of the plurality of energy storage and supplymeans 106 (as explained above in detailed description of FIG. 1 and FIG.2).

The storage module 316 stores the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data inthe database 204 a. In addition, the storage module 316 stores theplurality of statistical results and the log file having one or morecontrol schemes in the database 204 a. The database 204 a is associatedwith the server 204 of the recommendation system 122. Moreover, thestorage module 316 stores the first set of statistical data, the secondset of statistical data, the third set of statistical data, the fourthset of statistical data, the fifth set of statistical data, theplurality of statistical results and the log file having one or morecontrol schemes in real time.

The updating module 318 updates the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data. Inaddition, the updating module 318 updates the plurality of statisticalresults and the log file having one or more control schemes. Moreover,the recommendation system 122 updates the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data, the fifth set of statistical data,the plurality of statistical results and the log file having one or morecontrol schemes in real time.

The displaying module 320 displays the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data and the fifth set of statistical dataon the one or more statistical monitoring devices 126. In addition, thedisplaying module 320 displays the plurality of statistical results andthe log file having one or more control schemes on the one or morestatistical monitoring devices 126. Moreover, the displaying module 320displays the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results and the log file having one or more control schemesin real time.

FIG. 4A and FIG. 4B illustrate a flow chart 400 for intelligentlyrecommending the one or more control schemes to control peak loadingconditions and abrupt changes in energy pricing of the one or more builtenvironments, in accordance with various embodiments of the presentdisclosure. It may be noted that to explain the process steps offlowchart 400, references will be made to the system elements of FIG. 1,FIG. 2 and FIG. 3. It may also be noted that the flowchart 400 may havelesser or more number of steps.

The flowchart 400 initiates at step 402. Following step 402, at step404, the collection module 302 collects the first set of statisticaldata associated with the plurality of energy consuming devices 104present in the one or more built environments. At step 406, the fetchingmodule 304 fetches the second set of statistical data associated withthe occupancy behavior of the plurality of users 128 present inside eachof the one or more built environments. At step 408, the accumulationmodule 306 accumulates the third set of statistical data associated witheach of the plurality of energy storage and supply means 106. At step410, the reception module 308 receives the fourth set of statisticaldata associated with each of the plurality of environmental sensors 118.The flowchart 400 continues from step 412 as shown in FIG. 4B. Furtherat step 412, the gathering module 310 gathers the fifth set ofstatistical data associated with each of the plurality of energy pricingmodels 120. At step 414, the analyzing module 312 analyzes the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data and the fifthset of statistical data by performing the one or more statisticalfunctions. Further at step 416, the recommendation module 314 recommendsthe one or more control schemes to the plurality of energy consumingdevices 104 and the plurality of energy storage and supply means 106.The recommendation module 314 recommends the one or more control schemesfor controlling peak loading conditions and abrupt changes in energypricing. The flow chart 400 terminates at step 418.

FIG. 5 illustrates a block diagram of a computing device 500, inaccordance with various embodiments of the present disclosure. Thecomputing device 500 includes a bus 502 that directly or indirectlycouples the following devices: memory 504, one or more processors 506,one or more presentation components 508, one or more input/output (I/O)ports 510, one or more input/output components 512, and an illustrativepower supply 514. The bus 502 represents what may be one or more busses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 5 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Also, processors have memory. Theinventors recognize that such is the nature of the art, and reiteratethat the diagram of FIG. 5 is merely illustrative of an exemplarycomputing device 500 that can be used in connection with one or moreembodiments of the present invention. Distinction is not made betweensuch categories as “workstation,” “server,” “laptop,” “hand-helddevice,” etc., as all are contemplated within the scope of FIG. 5 andreference to “computing device.” The computing device 500 typicallyincludes a variety of computer-readable media. The computer-readablemedia can be any available media that can be accessed by the computingdevice 500 and includes both volatile and nonvolatile media, removableand non-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer storage media andcommunication media. The computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules or other data. Thecomputer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computing device 500. The communicationmedia typically embodies computer-readable instructions, datastructures, program modules or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

Memory 504 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory 504 may be removable,non-removable, or a combination thereof. Exemplary hardware devicesinclude solid-state memory, hard drives, optical-disc drives, etc. Thecomputing device 500 includes one or more processors that read data fromvarious entities such as memory 504 or I/O components 512. The one ormore presentation components 508 present data indications to a user orother device. Exemplary presentation components include a displaydevice, speaker, printing component, vibrating component, etc. The oneor more I/O ports 510 allow the computing device 500 to be logicallycoupled to other devices including the one or more I/O components 512,some of which may be built in. Illustrative components include amicrophone, joystick, game pad, satellite dish, scanner, printer,wireless device, etc.

The present disclosure has many advantages over the existing art. Thepresent disclosure provides technical advantages, economic advantages aswell as ancillary benefits. The present disclosure enables theutilization of the energy storage and supply means having relativelysmaller size and energy storage capacity for addressing the sametime-variant load reduction requirements. In addition, the presentdisclosure controls a large amount of energy loads or demands and costassociated with the installation and operations. Moreover, the presentdisclosure provides a stable grid network resulting in stable energypricing and removing volatility of current power system designs.

The foregoing descriptions of specific embodiments of the presenttechnology have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit thepresent technology to the precise forms disclosed, and obviously manymodifications and variations are possible in light of the aboveteaching. The embodiments were chosen and described in order to bestexplain the principles of the present technology and its practicalapplication, to thereby enable others skilled in the art to best utilizethe present technology and various embodiments with variousmodifications as are suited to the particular use contemplated. It isunderstood that various omissions and substitutions of equivalents arecontemplated as circumstance may suggest or render expedient, but suchare intended to cover the application or implementation withoutdeparting from the spirit or scope of the claims of the presenttechnology.

While several possible embodiments of the invention have been describedabove and illustrated in some cases, it should be interpreted andunderstood as to have been presented only by way of illustration andexample, but not by limitation. Thus, the breadth and scope of apreferred embodiment should not be limited by any of the above-describedexemplary embodiments.

What is claimed is:
 1. A computer-implemented method for intelligentlyrecommending one or more control schemes for controlling peak loadingconditions and abrupt changes in energy pricing associated withrenewable energy sources, the computer-implemented method comprising:collecting, at a recommendation system with a processor, a first set ofstatistical data including current power consumption and past powerconsumption of a plurality of energy consuming devices within one ormore built environments; fetching, at the recommendation system, asecond set of statistical data including an energy consumption behaviorof each of a plurality of users and an occupancy pattern of theplurality of users present inside the one or more built environmentsbased on occupancy sensors; accumulating, at the recommendation system,a third set of statistical data including current and historical energystorage and supply capacity data of a plurality of energy storage andsupply means of the one or more built environments including chargingrates, discharging rates, temperature characteristics, energy storageand release capacity of the plurality of energy storage and supplymeans; receiving, at the recommendation system, a fourth set ofstatistical data including current and historical temperature datainside and outside of the one or more built environments; gathering, atthe recommendation system, a fifth set of statistical data includingcurrent and historical recordings of energy pricing models affectingpower consumption of the one or more built environments; analyzing, atthe recommendation system the first set of statistical data, the secondset of statistical data, the third set of statistical data, the fourthset of statistical data, and the fifth set of statistical data using oneor more statistical functions to generate a plurality of statisticalresults, the plurality of statistical results including an energy usageprofile for each of the plurality of energy consuming devices, each ofthe plurality of energy storage devices, and each of the plurality ofusers; generating, at the recommendation system, the one or more controlschemes to control operations of the plurality of energy consumingdevices and the plurality of energy storage and supply means based onthe plurality of statistical results, the one or more control schemesincluding instructions for optimizing control of the plurality of energyconsuming devices based on the first set of statistical data, the secondset of statistical data, and the third set of statistical data; andautomatically controlling the operation of the plurality of energyconsuming devices and the energy storage and supply means based on theone or more control schemes.
 2. The computer-implemented method of claim1, the one or more control schemes being recommended to identify andcounter unusual and unexpected energy behaviors in energy consumption ofeach of the plurality of energy consuming devices, and wherein therecommendation being performed by dynamically comparing loadingconditions and energy pricing of the one or more built environments withpast loading conditions and energy pricing after application of the oneor more control schemes.
 3. The computer-implemented method of claim 1,further comprising storing, at the recommendation system with theprocessor, the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results and a log file having the one or more controlschemes in a database.
 4. The computer-implemented method of claim 1,further comprising updating, at the recommendation system with theprocessor, the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results and a log file having the one or more controlschemes.
 5. The computer-implemented method of claim 1, furthercomprising displaying, at the recommendation system with the processor,the first set of statistical data, the second set of statistical data,the third set of statistical data, the fourth set of statistical data,the fifth set of statistical data, the plurality of statistical resultsand a log file having the one or more control schemes.
 6. Thecomputer-implemented method of claim 1, wherein the one or morestatistical functions comprises: translating the current operationalstate data and the past operational state data associated with theplurality of energy consuming devices into energy demand values for apre-defined interval of time; parsing the first set of statistical data,the second set of statistical data and the third set of statisticaldata; auto-fulfilling one or more data entries in the first set ofstatistical data, the second set of statistical data and the third setof statistical data based on a self-learning algorithm; and comparingthe current operational state data with the past operational state datato determine a potential for improvement in energy consumption of eachof the plurality of energy consuming devices.
 7. Thecomputer-implemented method of claim 1, wherein the plurality ofstatistical results comprises one or more graphs, one or more charts,one or more tables and one or more statistical maps of energyconsumption as a function of duration of operations of the plurality ofenergy consuming devices, energy storage and supply capacity of theplurality of energy storage and supply means, environmental conditionsand energy pricing affecting the one or more built environments.
 8. Thecomputer-implemented method of claim 1, wherein the one or more controlschemes being applied by utilizing a network based automatic controlsystem associated with each of the one or more built environments andwherein the one or more control schemes being automatically appliedusing the network based automatic control system.
 9. Thecomputer-implemented method of claim 1, wherein the instructionscomprises: regulating power supply of each of the plurality of energyconsuming devices based on an occupancy pattern, energy demand andarchitectural design of the one or more built environments; regulatingenergy consumption duration of the plurality of energy consumingdevices; notifying a list of malfunctioning devices of the plurality ofenergy consuming devices; performing an operation on the plurality ofenergy consuming devices, the operation being selected from a group ofoperations consisting of upgrading, downgrading, replacing and repairingof the plurality of energy consuming devices; prompting the plurality ofenergy storage and supply means to start and stop charge cycles atspecific time periods for reducing energy consumption costs; promptingthe plurality of energy storage and supply means to start and stopdischarge cycles for controlling peak loading periods; and regulatingcharging and discharging characteristics of each of the plurality ofenergy storage and supply means.
 10. The computer-implemented method ofclaim 1, wherein the first plurality of parameters comprises a set ofoperational characteristics associated with each of the plurality ofenergy consuming devices and a set of physical characteristicsassociated with each of the plurality of energy consuming devices,wherein the set of operational characteristics comprises a currentrating, a voltage rating, a power rating, a frequency of operation, anoperating temperature, a device temperature, the duration of operation,a seasonal variation in operation and off-seasonal variation inoperation and wherein the set of physical characteristics comprises adevice size, a device area, a device physical location and a portabilityof device.
 11. The computer-implemented method of claim 1, wherein athird plurality of parameters associated with the fourth set ofstatistical data comprises a metric for recording environmental datacomprising temperature, humidity and air pressure associated with eachof the plurality of environmental sensors present inside or outside theone or more built environments and wherein the environmental data beingobtained from a plurality of external application programming interfacesand a plurality of third party databases.
 12. The computer-implementedmethod of claim 1, wherein a fourth plurality of parameters associatedwith the fifth set of statistical data comprises a metric for recordingenergy pricing data including the energy pricing model or an energyprice signal associated with the one or more built environments andwherein the energy pricing data being obtained from a plurality ofexternal application programming interfaces and a plurality of thirdparty databases.
 13. A computer system comprising: one or moreprocessors; and a memory coupled to the one or more processors, thememory for storing instructions which, when executed by the one or moreprocessors, cause the one or more processors to: collect a first set ofstatistical data including current power consumption and past powerconsumption of a plurality of energy consuming devices within one ormore built environments; fetch a second set of statistical dataincluding an energy consumption behavior of each of a plurality of usersand an occupancy pattern of the plurality of users present inside theone or more built environments based on occupancy sensors; accumulate athird set of statistical data including current and historical energystorage and supply capacity data of a plurality of energy storage andsupply means of the one or more built environments including chargingrates, discharging rates, temperature characteristics, energy storageand release capacity of the plurality of energy storage and supplymeans; receive a fourth set of statistical data including current andhistorical temperature data inside and outside of the one or more builtenvironments; gather a fifth set of statistical data including currentand historical recordings of energy pricing models affecting powerconsumption of the one or more built environments; analyze the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data, and the fifthset of statistical data using one or more statistical functions togenerate a plurality of statistical results, the plurality ofstatistical results including an energy usage profile for each of theplurality of energy consuming devices, each of the plurality of energystorage devices, and each of the plurality of users; generate the one ormore control schemes to control operations of the plurality of energyconsuming devices and the plurality of energy storage and supply meansbased on the plurality of statistical results, the one or more controlschemes including instructions for optimizing control of the pluralityof energy consuming devices based on the first set of statistical data,the second set of statistical data, and the third set of statisticaldata; and automatically control the operation of the plurality of energyconsuming devices and the energy storage and supply means based on theone or more control schemes.
 14. The computer system of claim 13,further comprising storing, at the recommendation system, the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data, the fifth setof statistical data, the plurality of statistical results and a log filehaving the one or more control schemes in a database.
 15. The computersystem of claim 13, further comprising updating, at the recommendationsystem, the first set of statistical data, the second set of statisticaldata, the third set of statistical data, the fourth set of statisticaldata, the fifth set of statistical data, the plurality of statisticalresults and a log file having the one or more control schemes.
 16. Thecomputer system of claim 13, further comprising displaying, at therecommendation system, the first set of statistical data, the second setof statistical data, the third set of statistical data, the fourth setof statistical data, the fifth set of statistical data, the plurality ofstatistical results and a log file having the one or more controlschemes on one or more statistical monitoring devices.
 17. Anon-transitory computer-readable storage medium encoding computerexecutable instructions that, when executed by at least one processor,cause the processor to: collect a first set of statistical dataincluding current power consumption and past power consumption of aplurality of energy consuming devices within one or more builtenvironments; fetch a second set of statistical data including an energyconsumption behavior of each of a plurality of users and an occupancypattern of the plurality of users present inside the one or more builtenvironments based on occupancy sensors; accumulate a third set ofstatistical data including current and historical energy storage andsupply capacity data of a plurality of energy storage and supply meansof the one or more built environments including charging rates,discharging rates, temperature characteristics, energy storage andrelease capacity of the plurality of energy storage and supply means;receive a fourth set of statistical data including current andhistorical temperature data inside and outside of the one or more builtenvironments; gather a fifth set of statistical data including currentand historical recordings of energy pricing models affecting powerconsumption of the one or more built environments; analyse the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data, and the fifthset of statistical data using one or more statistical functions togenerate a plurality of statistical results, the plurality ofstatistical results including an energy usage profile for each of theplurality of energy consuming devices, each of the plurality of energystorage devices, and each of the plurality of users; generate the one ormore control schemes to control operations of the plurality of energyconsuming devices and the plurality of energy storage and supply meansbased on the plurality of statistical results, the one or more controlschemes including instructions for optimizing control of the pluralityof energy consuming devices based on the first set of statistical data,the second set of statistical data, and the third set of statisticaldata; and automatically controlling the operation of the plurality ofenergy consuming devices and the energy storage and supply means basedon the one or more control schemes.
 18. The non-transitory computerreadable storage medium of claim 17, further comprising instructions forstoring at the computing device, the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data, the fifth set of statistical data, theplurality of statistical results and a log file having one or morecontrol schemes in a database, wherein the storing being done in realtime.
 19. The non-transitory computer readable storage medium of claim17, further comprising instructions for updating at the computingdevice, the first set of statistical data, the second set of statisticaldata, the third set of statistical data, the fourth set of statisticaldata, the fifth set of statistical data, the plurality of statisticalresults and a log file having one or more control schemes, wherein theupdating being done in real time.